In this paper, a swarm intelligence optimization algorithm is proposed as the Shrike Optimization Algorithm (SHOA). Many creatures living in a group and surviving for the next generation randomly search for foods; they follow the best one in the swarm, called swarm intelligence. Swarm-based algorithms are designed to mimic creatures' behaviors, but in the multi-modal problem competition, they lack the ability to find optimal solutions in many cases. The main inspiration for the proposed algorithm is taken from the swarming behavior of shrike birds in nature. The shrike birds are migrating from their territory to survive. However, the SHOA mimics the surviving behavior of shrike birds for living, adaptation, and increasing. Two parts of optimization exploration and exploitation are designed by modeling shrike breeding and searching for foods to feed nestlings until they get ready to fly and live independently. This paper is a mathematical model for the SHOA to perform optimization. The SHOA benchmarked 29 competitive, well-known mathematical test functions and four real-world engineering problems with different conditions, both constrained and unconstrained. The statistical results show the proposed SHOA can perform nearly ideal results when compared with other well-known algorithms for multi-modal problems. The results for engineering optimization problems show the SHOA outperforms other nature-inspired algorithms.